Re: [music-dsp] Who uses YIN or pYIN for pitch detection?
Pyin seems nice, I never tasted it before ! Yeah I use YIN, I made a sacred way searching and writing Pitch Tracking algorithm to works with Keith Lent Pitch Shifting, I have coded the basic steps of YIN in matlab, but for me or my implementation not works so well like AMDF or some improved ACF... BTW the author of paper seems that put the source code online here https://code.soundsoftware.ac.uk/attachments/download/1458/pyin-v1.1.tar.gz Regards, Eder de Souza On Thu, Mar 7, 2019 at 1:47 AM Ethan Duni wrote: > Looks like they use the Viterbi algorithm to get the pitch tracks. > > On Mar 6, 2019, at 6:59 PM, Jay wrote: > > > Looks like there's a link to a python implementation on this topics page, > might provide some insights: > https://github.com/topics/pitch-tracking > > > > > > > > > On Wed, Mar 6, 2019 at 6:44 PM robert bristow-johnson < > r...@audioimagination.com> wrote: > >> >> >> Hay, any peeps around here that use YIN? or pYIN? >> >> Some of you who hang around the DSP Stack Exchange might know that I am >> unimpressed with YIN, namely that I don't think there is anything novel >> about it (w.r.t. Average Squared Difference Function, ASDF) other than this >> so-called "Cumulative Mean Normalized Difference Function"(CMNDF) which >> seems to have the only purpose to prevent choosing the lag of 0 as the >> best-fit lag. Big Dl. There are other ways to do that, and otherwise >> the CMNDF just fucks up the ASDF result, at least a little, at the lags >> around the period length. And ASDF is still the measure of best fit. Here >> is where I complain a little about YIN: >> > >> https://dsp.stackexchange.com/questions/51823/yin-pitch-estimation-algoritm-simplified-explanation/51842#51842 >> >> Here is a copy of the original YIN: >> > [Cheveigne A, Kawahara H. - *YIN, a fundamental frequency estimator for >> speech and music*](http://audition.ens.fr/adc/pdf/2002_JASA_YIN.pdf ) >> >> and the new, improved probabilistic YIN: >> > [Mauch M, Dixon S. - *PYIN: A fundamental frequency estimator using >> probabilistic threshold distributions*]( >> http://matthiasmauch.de/_pdf/mauch_pyin_2014.pdf ) >> >> Now, while I don't want to use YIN to find pitch candidates (I think I do >> a better job of it with just the ASDF), I am curious about pYIN in what >> exactly they do with the pitch candidates. I understand Hidden Markov >> Models, or at least I used to, but I do not know what Mauch and Dixon do to >> actually pick the final candidate. Has anyone else slogged through this >> enough to understand what hey are doing? How do they connect a candidate >> from the previous frame to a candidate of the current frame?, and then, how >> does pYIN score each candidate and choose the candidate that will be output >> as the pitch? >> >> If anyone worked on this, please lemme know. >> >> >> -- >> >> r b-j r...@audioimagination.com >> >> "Imagination is more important than knowledge." >> >> >> >> >> >> >> >> ___ >> dupswapdrop: music-dsp mailing list >> music-dsp@music.columbia.edu >> https://lists.columbia.edu/mailman/listinfo/music-dsp > > ___ > dupswapdrop: music-dsp mailing list > music-dsp@music.columbia.edu > https://lists.columbia.edu/mailman/listinfo/music-dsp > > ___ > dupswapdrop: music-dsp mailing list > music-dsp@music.columbia.edu > https://lists.columbia.edu/mailman/listinfo/music-dsp ___ dupswapdrop: music-dsp mailing list music-dsp@music.columbia.edu https://lists.columbia.edu/mailman/listinfo/music-dsp
Re: [music-dsp] Who uses YIN or pYIN for pitch detection?
Looks like they use the Viterbi algorithm to get the pitch tracks. > On Mar 6, 2019, at 6:59 PM, Jay wrote: > > > Looks like there's a link to a python implementation on this topics page, > might provide some insights: > https://github.com/topics/pitch-tracking > > > > > > > > >> On Wed, Mar 6, 2019 at 6:44 PM robert bristow-johnson >> wrote: >> >> >> Hay, any peeps around here that use YIN? or pYIN? >> >> Some of you who hang around the DSP Stack Exchange might know that I am >> unimpressed with YIN, namely that I don't think there is anything novel >> about it (w.r.t. Average Squared Difference Function, ASDF) other than this >> so-called "Cumulative Mean Normalized Difference Function"(CMNDF) which >> seems to have the only purpose to prevent choosing the lag of 0 as the >> best-fit lag. Big Dl. There are other ways to do that, and otherwise >> the CMNDF just fucks up the ASDF result, at least a little, at the lags >> around the period length. And ASDF is still the measure of best fit. Here >> is where I complain a little about YIN: >> > https://dsp.stackexchange.com/questions/51823/yin-pitch-estimation-algoritm-simplified-explanation/51842#51842 >> > >> >> Here is a copy of the original YIN: >> > [Cheveigne A, Kawahara H. - *YIN, a fundamental frequency estimator for >> > speech and music*](http://audition.ens.fr/adc/pdf/2002_JASA_YIN.pdf ) >> >> and the new, improved probabilistic YIN: >> > [Mauch M, Dixon S. - *PYIN: A fundamental frequency estimator using >> > probabilistic threshold >> > distributions*](http://matthiasmauch.de/_pdf/mauch_pyin_2014.pdf ) >> Now, while I don't want to use YIN to find pitch candidates (I think I do a >> better job of it with just the ASDF), I am curious about pYIN in what >> exactly they do with the pitch candidates. I understand Hidden Markov >> Models, or at least I used to, but I do not know what Mauch and Dixon do to >> actually pick the final candidate. Has anyone else slogged through this >> enough to understand what hey are doing? How do they connect a candidate >> from the previous frame to a candidate of the current frame?, and then, how >> does pYIN score each candidate and choose the candidate that will be output >> as the pitch? >> >> If anyone worked on this, please lemme know. >> >> >> -- >> >> r b-j r...@audioimagination.com >> >> "Imagination is more important than knowledge." >> >> >> >> >> >> >> >> >> ___ >> dupswapdrop: music-dsp mailing list >> music-dsp@music.columbia.edu >> https://lists.columbia.edu/mailman/listinfo/music-dsp > ___ > dupswapdrop: music-dsp mailing list > music-dsp@music.columbia.edu > https://lists.columbia.edu/mailman/listinfo/music-dsp ___ dupswapdrop: music-dsp mailing list music-dsp@music.columbia.edu https://lists.columbia.edu/mailman/listinfo/music-dsp
Re: [music-dsp] Who uses YIN or pYIN for pitch detection?
Looks like there's a link to a python implementation on this topics page, might provide some insights: https://github.com/topics/pitch-tracking On Wed, Mar 6, 2019 at 6:44 PM robert bristow-johnson < r...@audioimagination.com> wrote: > > > Hay, any peeps around here that use YIN? or pYIN? > > Some of you who hang around the DSP Stack Exchange might know that I am > unimpressed with YIN, namely that I don't think there is anything novel > about it (w.r.t. Average Squared Difference Function, ASDF) other than this > so-called "Cumulative Mean Normalized Difference Function"(CMNDF) which > seems to have the only purpose to prevent choosing the lag of 0 as the > best-fit lag. Big Dl. There are other ways to do that, and otherwise > the CMNDF just fucks up the ASDF result, at least a little, at the lags > around the period length. And ASDF is still the measure of best fit. Here > is where I complain a little about YIN: > > > https://dsp.stackexchange.com/questions/51823/yin-pitch-estimation-algoritm-simplified-explanation/51842#51842 > > Here is a copy of the original YIN: > > [Cheveigne A, Kawahara H. - *YIN, a fundamental frequency estimator for > speech and music*](http://audition.ens.fr/adc/pdf/2002_JASA_YIN.pdf ) > > and the new, improved probabilistic YIN: > > [Mauch M, Dixon S. - *PYIN: A fundamental frequency estimator using > probabilistic threshold distributions*]( > http://matthiasmauch.de/_pdf/mauch_pyin_2014.pdf ) > > Now, while I don't want to use YIN to find pitch candidates (I think I do > a better job of it with just the ASDF), I am curious about pYIN in what > exactly they do with the pitch candidates. I understand Hidden Markov > Models, or at least I used to, but I do not know what Mauch and Dixon do to > actually pick the final candidate. Has anyone else slogged through this > enough to understand what hey are doing? How do they connect a candidate > from the previous frame to a candidate of the current frame?, and then, how > does pYIN score each candidate and choose the candidate that will be output > as the pitch? > > If anyone worked on this, please lemme know. > > > -- > > r b-j r...@audioimagination.com > > "Imagination is more important than knowledge." > > > > > > > > ___ > dupswapdrop: music-dsp mailing list > music-dsp@music.columbia.edu > https://lists.columbia.edu/mailman/listinfo/music-dsp ___ dupswapdrop: music-dsp mailing list music-dsp@music.columbia.edu https://lists.columbia.edu/mailman/listinfo/music-dsp
[music-dsp] Who uses YIN or pYIN for pitch detection?
Hay, any peeps around here that use YIN? or pYIN? Some of you who hang around the DSP Stack Exchange might know that I am unimpressed with YIN, namely that I don't think there is anything novel about it (w.r.t. Average Squared Difference Function, ASDF) other than this so-called "Cumulative Mean Normalized Difference Function"(CMNDF) which seems to have the only purpose to prevent choosing the lag of 0 as the best-fit lag. Big Dl. There are other ways to do that, and otherwise the CMNDF just fucks up the ASDF result, at least a little, at the lags around the period length. And ASDF is still the measure of best fit. Here is where I complain a little about YIN: > >https://dsp.stackexchange.com/questions/51823/yin-pitch-estimation-algoritm-simplified-explanation/51842#51842 > Here is a copy of the original YIN: > [Cheveigne A, Kawahara H. - *YIN, a fundamental frequency estimator for speech and music*](http://audition.ens.fr/adc/pdf/2002_JASA_YIN.pdf ) and the new, improved probabilistic YIN: > [Mauch M, Dixon S. - *PYIN: A fundamental frequency estimator using probabilistic threshold distributions*](http://matthiasmauch.de/_pdf/mauch_pyin_2014.pdf ) Now, while I don't want to use YIN to find pitch candidates (I think I do a better job of it with just the ASDF), I am curious about pYIN in what exactly they do with the pitch candidates. I understand Hidden Markov Models, or at least I used to, but I do not know what Mauch and Dixon do to actually pick the final candidate. Has anyone else slogged through this enough to understand what hey are doing? How do they connect a candidate from the previous frame to a candidate of the current frame?, and then, how does pYIN score each candidate and choose the candidate that will be output as the pitch? If anyone worked on this, please lemme know. -- r b-j r...@audioimagination.com "Imagination is more important than knowledge." ___ dupswapdrop: music-dsp mailing list music-dsp@music.columbia.edu https://lists.columbia.edu/mailman/listinfo/music-dsp
